Guest Editorial: Distributed Simulation, Virtual Environments and Real‐time Applications
Bibliographic record
Abstract
Welcome to this special issue of Concurrency and Computation on 'Distributed Simulation, Virtual Environments and Real-time Applications'.The papers included in this special issue are based mainly on selected extended versions of the best papers accepted in the 2007 International Symposium on Distributed Simulation and Real Time Applications (DS-RT 2007).We have only accepted five papers in this special issue from many selected extended versions of papers accepted in DS-RT.This means that these accepted papers have undergone a thorough and a critical review process by experts in the field.In their work Boukerche, Al Hamidi and Zhang described the design and implementation of a web-based 3D virtual environment application that aims to help the training practice of personnel working in the radiology department of a hospital.Their proposed system aims at helping users to learn, to play or to be trained in a 'like-real' situation by using X3D technology and embedding it in a distributed virtual environment.Murray, Roberts, Steed, Sharkey, Dickerson, Rae and Wolff addressed an important issue related to collaborative virtual environments and video conferencing.They are enhancing the 3D representation of participants through avatars by mechanisms tracking people's eyes and representing their movements on their avatars.Their approach is capable of reproducing the line of gaze as opposed to approximating it only.They also describe an experiment to assess the difference between users' abilities to judge what objects an avatar is looking at with only head movements being displayed and the eyes remaining static, and with eye gaze and head movement information being displayed.The results from the experiment showed that eye gaze is of vital importance to the subjects correctly identifying what a person is looking at in an immersive virtual environment.Hossain and El Saddik addressed several key issues in distributed multimedia services management and composition such as scalability, heterogeneity and QoS.Their proposed framework introduced biologically inspired multimedia service management through the composition of different multimedia services such as streaming and transcoding services.They make use of biologically inspired algorithms in order to collect the QoS requirements from individual transcoding services that fulfill the requirements of the composition process.A prototype of the proposed framework is designed, implemented and evaluated in terms of scalability and load balancing.Lees, Logan and Theodoropoulos described an optimistic synchronisation algorithm for parallel discrete event simulation of multi-agent systems, and showed that it outperforms Time Warp and
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.037 | 0.021 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".